Synchronization of repetitive therapeutic interventions

ABSTRACT

A medical device of the type used for assisting a user in manually delivering repetitive therapy to a patient (e.g., chest compressions or ventilations in cardiac resuscitation), the device comprising a feedback device configured to generate feedback cues to assist the user in timing the delivery of the repetitive therapy, at least one sensor or circuit element configured to detect actual delivery times, at which the user actually delivers the repetitive therapy, and a processor, memory, and associated circuitry configured to compare the actual delivery times to information representative of desired delivery times to determine cue times at which the feedback cues are generated by the feedback device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of and claims priority toU.S. application Ser. No. 12/639,133, filed on Dec. 16, 2009, whichapplication is a divisional application of and claims priority to U.S.application Ser. No. 11/227,968, filed on Sep. 14, 2005 now U.S. Pat.No. 7,650,181. These applications are hereby incorporated by reference.

TECHNICAL FIELD

This invention relates to the field of medical devices for assisting indelivery of repetitive therapy, such as assisting rescuers in performingcardio-pulmonary resuscitation (CPR).

BACKGROUND

Resuscitation treatments for patients suffering from cardiac arrestgenerally include clearing and opening the patient's airway, providingrescue breathing for the patient, and applying chest compressions toprovide blood flow to the victim's heart, brain and other vital organs.If the patient has a shockable heart rhythm, resuscitation also mayinclude defibrillation therapy. The term basic life support (BLS)involves all the following elements: initial assessment; airwaymaintenance; expired air ventilation (rescue breathing); and chestcompression. When all three [airway breathing, and circulation,including chest compressions] are combined, the term cardiopulmonaryresuscitation (CPR) is used.

There are many different kinds of abnormal heart rhythms, some of whichcan be treated by defibrillation therapy (“shockable rhythms”) and somewhich cannot (non-shockable rhythms”). For example, most ECG rhythmsthat produce significant cardiac output are considered non-shockable(examples include normal sinus rhythms, certain bradycardias, and sinustachycardias). There are also several abnormal ECG rhythms that do notresult in significant cardiac output but are still considerednon-shockable, since defibrillation treatment is usually ineffectiveunder these conditions. Examples of these non-shockable rhythms includeasystole, electromechanical disassociation (EMD) and other pulselesselectrical activity (PEA). Although a patient cannot remain alive withthese non-viable, non-shockable rhythms, applying shocks will not helpconvert the rhythm. The primary examples of shockable rhythms, for whichthe caregiver should perform defibrillation, include ventricularfibrillation, ventricular tachycardia, and ventricular flutter.

After using a defibrillator to apply one or more shocks to a patient whohas a shockable ECG rhythm, the patient may nevertheless remainunconscious, in a shockable or non-shockable, perfusing or non-perfusingrhythm. If a non-perfusing rhythm is present, the caregiver may thenresort to performing CPR for a period of time in order to providecontinuing blood flow and oxygen to the patient's heart, brain and othervital organs. If a shockable rhythm continues to exist or developsduring the delivery of CPR, further defibrillation attempts may beundertaken following this period of cardiopulmonary resuscitation. Aslong as the patient remains unconscious and without effectivecirculation, the caregiver can alternate between use of thedefibrillator (for analyzing the electrical rhythm and possibly applyinga shock) and performing cardio-pulmonary resuscitation (CPR). CPRgenerally involves a repeating pattern of five or fifteen chestcompressions followed by a pause during which two rescue breaths aregiven.

Defibrillation can be performed using an AED. The American HeartAssociation, European Resuscitation Council, and other similar agenciesprovide protocols for the treatment of victims of cardiac arrest thatinclude the use of AEDs. These protocols define a sequence of steps tobe followed in accessing the victim's condition and determining theappropriate treatments to be delivered during resuscitation. Caregiverswho may be required to use an AED are trained to follow these protocols.

Most automatic external defibrillators are actually semi-automaticexternal defibrillators (SAEDs), which require the caregiver to press astart or analyze button, after which the defibrillator analyzes thepatient's ECG rhythm and advises the caregiver to provide a shock to thepatient if the electrical rhythm is shockable. The caregiver is thenresponsible for pressing a control button to deliver the shock.Following shock delivery, the SAED may reanalyze the patient's ECGrhythm, automatically or manually, and advise additional shocks orinstruct the caregiver to check the patient for signs of circulation(indicating that the defibrillation treatment was successful or that therhythm is non-shockable) and to begin CPR if circulation has not beenrestored by the defibrillation attempts. Fully automatic externaldefibrillators, on the other hand, do not wait for user interventionbefore applying defibrillation shocks. As used below, automatic externaldefibrillators (AED) include semi-automatic external defibrillators(SAED).

Both types of defibrillators typically provide an auditory “stand clear”warning before beginning ECG analysis and/or the application of eachshock. The caregiver is then expected to stand clear of the patient(i.e. stop any physical contact with the patient) and may be required topress a button to deliver the shock. The controls for automatic externaldefibrillators are typically located on a resuscitation device housing.

AEDs are typically used by trained medical or paramedic caregivers, suchas physicians, nurses, emergency medical technicians, fire departmentpersonnel, and police officers. The ready availability of on-site AEDsand caregivers trained to operate them is important because a patient'schances of survival from cardiac arrest decrease by approximately 10%for each minute of delay between occurrence of the arrest and thedelivery of defibrillation therapy.

Trained lay caregivers are a new group of AED operators. For example,spouses of heart attack victims may become trained as lay caregivers.Lay caregivers rarely have opportunities to defibrillate or deliver CPR,and thus they can be easily intimidated by an AED during a medicalemergency. Consequently, such lay providers may be reluctant to purchaseor use AEDs when needed, or might tend to wait for an ambulance toarrive rather than use an available AED, out of concern that the layprovider might do something wrong.

Some trained medical providers, e.g., specialists such as obstetricians,dermatologists, and family care practitioners, also rarely have theopportunity to perform CPR and/or defibrillate, and thus may be uneasyabout doing so. Concerns about competence are exacerbated if training isinfrequent, leading the caregiver to worry that he or she may not beable to remember all of the recommended resuscitation protocol stepsand/or their correct sequence.

Similarly, both medical and lay caregivers may be hesitant to provideCPR and rescue breathing, or may be unsure when these steps should beperformed, particularly if their training is infrequent and they rarelyhave the opportunity to use it.

It is well known to those skilled in the art, and has been shown in anumber of studies, that CPR is a complex task with both poor initiallearning as well as poor skill retention, with trainees often losing 80%of their initial skills within 6-9 months. It has thus been the objectof a variety of prior art to attempt to improve on this disadvantageouscondition. Aids in the performance of chest compressions are describedin U.S. Pat. Nos. 4,019,501, 4,077,400, 4,095,590, 5,496,257, 6,125,299,and 6,306,107, 6,390,996. U.S. Pat. Nos. 4,588,383, 5,662,690 5,913,685,and 4,863,385 describe CPR prompting systems. AEDs have always includedvoice prompts as well as graphical instructions on flip charts orplacards since the earliest commercial versions in 1974, to provide bothcorrect timing and sequence for the complex series of actions requiredof the rescuer as well as placement of the defibrillation electrodes.U.S. patent application Ser. No. 09/952,834 and U.S. Pat. Nos. 6,334,070and 6,356,785 describe defibrillators with an increased level ofprompting including visual prompts either in the form of graphicalinstructions presented on a CRT or on printed labels with backlightingor emissive indicia such as light emitting diodes. AEDs since the 1970shave used the impedance measured between the defibrillation electrodesto determine the state of the AED as well as appropriate messages todeliver to the rescuer (e.g., “Attach Electrodes” if the initial promptson the unit have been delivered and the impedance remains greater thansome specified threshold; or to determine if there is excessive patientmotion as in U.S. Pat. No. 4,610,254). U.S. Pat. No. 5,700,281 describesa device which uses the impedance of the electrodes to determine thestate of the AED for delivering such messages as “Attach Electrodes.”

Enhanced prompting embodied in these patents provides some benefit tothe rescuer in improved adherence to the complex protocol required ofthem to successfully revive a cardiac arrest patient, but it has beendiscovered in testing of the AEDs generally employing elements of thesepatents that rescuers are still only able to achieve a performance levelof less than about 50%. The methods of the study were as follows: Noneof the subjects had prior experience or training with an AED in order toeliminate the potential for bias due to previous AED training. The testsubjects were presented with a simulated use scenario more accuratelyresembling than in previous studies what a lay rescuer would encounterin a cardiac arrest rescue situation. Four fully-functionaldefibrillators were used: Physio-Control LifePak CR Plus, ZOLL AED Plus,the Philips/Laerdal HeartStart OnSite, and the Cardiac SciencePowerHeart. The test subjects were led into a simulated office, and toldthat a person, simulated by a manikin, had just fallen to the floor,appeared to be completely unconscious and could well be dying. They weretold to use the AED and any other object in the office and act as if itwere a real emergency. Each person was evaluated based on the number ofactions taken that comprise the Chain of Survival Sequence (8 steps:check response, seek help, open airway, check breathing, give breathes,check circulation, remove clothing, and attach AED electrodes). It wasfound that the Medtronic (Minnesota) Lifepak CR Plus group, whichcomprised 11 lay rescuers, averaged 3.5±1.4 steps completed; the CardiacScience (California) PowerHeart group, which comprised 11 lay rescuers,averaged 3.4±1.9 steps; the Philips (Massachusetts) HeartStart group,which comprised 12 lay rescuers, averaged 3.8±1.3 steps; and the ZOLL(Massachusetts) AEDPlus group, which comprised 11 lay rescuers, averaged5.0±1.3 steps completed. Even the ZOLL device that was shown to bestatistically better than the other devices only achieved a 63%compliance rate. Further, less than 10% of the test subjects were ableto sustain the recommended 100 compressions per minute for at least oneminutes' duration.

It has recently been recognized that good CPR is essential to savingmore victims of cardiac arrest (Circulation. 2005; 111:428-434). In thecited study, researchers found that in 36.9% of the total number ofsegments, compression rates were less than 80 compressions per minute(cpm), and 21.7% had rates of less than 70 cpm. The compression raterecommended by the American Heart Association in their guidelines isgreater than 100 cpm. In the study, higher chest compression rates weresignificantly correlated with initial return of spontaneous circulation(mean chest compression rates for initial survivors and non-survivors,90±17 and 79±18 cpm, respectively; P=0.0033). Further, this study wasperformed using well-trained rescuers, including nurses and physicians,indicating that the problem of poor compression rates is widespread.

AEDs with CPR feedback such as those of ZOLL and Philips mentioned abovehave some form of compression rate prompting. This takes the form of abeep or tone at the desired rate of 100 compressions per minute asrecommended by the American Heart Association guidelines. The ZOLLAEDPlus has the added feature that it will begin the compression ratetones at the rate that the rescuer begins their compressions, and thengradually increases the compression tone rate up to the desired rate of100 cpm. In some cases, this approach may be helpful, but because thecompression tone rate is asynchronous with the rescuer's compressions,the tones may occur out of phase with the rescuer compression rate, andmay actually act to confuse the rescuer and momentarily slow them down.

AEDs have also been solely focused on defibrillation, which, while itprovides the best treatment for ventricular fibrillation and certaintachycardias, is of no therapeutic benefit for the 60% of the cardiacarrest patients presenting in pulseless electrical activity (PEA) orasystole. As AEDs are becoming more prevalent in the home, there arealso a host of other health problems that occur such as first aid aswell as incidents related to chronic conditions such as asthma, diabetesor cardiac-related conditions for which the AED is of no benefit.

After a defibrillation shock, the heart is in one of two states: eitherthe shock was successful and the heart is in a stunned, ischemiccondition with very little myocardial ATP energy reserves necessary forrhythmic pacemaker activity and effective cardiac output, or the shockwas unsuccessful. Surprisingly to some, a defibrillation rarely, ifever, converts ventricular fibrillation into a normal sinus rhythm witheffective hemodynamic output. Good CPR is required after a successfuldefibrillation shock in order for a patient to survive.

Although automated chest compression devices, such as that described byU.S. Pat. No. 6,752,771 have been synchronized with the cardiac cycle,rescuers providing manual CPR generally compresses the chest at a fixedrate with no synchronization to the cardiac cycle of a damaged heartsuch as occurs with pulseless electrical activity (PEA). PEA is acondition where the heart is functioning electrically, but does not haveenough healthy muscle fibers to contract effectively. Patients typicallyhave a very low ejection fraction where most of the blood in the heartremains in the ventricles during the contraction rather than beingejected in to the aorta and coronary arteries.

Many studies have reported that the discontinuation of chestcompressions, such as is commonly done for ECG analysis, cansignificantly reduce the recovery rate of spontaneous circulation and24-hour survival rate. These studies include “Adverse effects ofinterrupting precordial compression during cardiopulmonaryresuscitation” by Sato et al. (Critical Care Medicine, Volume 25(5), May1997, pp 733-736); “Adverse Outcomes of Interrupted PrecordialCompression During Automated Defibrillation” by Yu et al. (Circulation,2002); and “Predicting Outcome of Defibrillation by SpectralCharacterization and Nonparametric Classification of VentricularFibrillation in Patients With Out-of-Hospital Cardiac Arrest” byEftestøl et al. (Circulation, 2002).

In the context of automatic, mechanical compression systems, it has longbeen recognized that there are beneficial effects of synchronizingcardiac compression and ventilation cycles to the cardiac cycle. M. R.Pinsky, “Hemodynamic effects of cardiac cycle-specific increases inintrathoracic pressure”, Journal of Applied Physiology (Volume 60(2),pages 604-612, February 1986). U.S. Pat. Nos. 4,273,114, 4,326,507, and6,752,771 describe mechanical compression systems that synchronize thecompression cycle to the cardiac cycle. U.S. Patent application2004/0162587 describes a mechanical compression system that modifies thechest compression based on monitored blood perfusion.

In U.S. Pat. No. 4,491,423 a resuscitation assistive timer is describedthat provides an audible compression rate that is adjusted based on thepatient's age.

SUMMARY

In general, in a first aspect, the invention features a medical deviceof the type used for assisting a user in manually delivering repetitivetherapy to a patient (e.g., chest compressions or ventilations incardiac resuscitation), the device comprising a feedback deviceconfigured to generate feedback cues to assist the user in timing thedelivery of the repetitive therapy, at least one sensor or circuitelement configured to detect actual delivery times, at which the useractually delivers the repetitive therapy, and a processor, memory, andassociated circuitry configured to compare the actual delivery times toinformation representative of desired delivery times to determine cuetimes at which the feedback cues are generated by the feedback device.

In preferred implementations, one or more of the following features maybe incorporated. The manually delivered repetitive therapy may comprisemanually delivered chest compressions as part of cardiac resuscitation.The actual delivery times and desired delivery times may comprise actualdelivery phases and desired delivery phases. Actual delivery times anddesired delivery times may be compared by forming differences betweenactual and desired delivery times. The cue times may be graduallyshifted in time over a plurality of therapy deliveries to graduallyshift the actual delivery times. The feedback cues may be audible orvisual. The repetitive therapy may further comprise ventilation as partof cardiac resuscitation. The feedback cues may have at least two phasesdistinguishable by the user, with a first phase corresponding to a firstphase of the delivered therapy, and a second phase of the feedback cuecorresponding to a second phase of the delivered therapy. The repetitivetherapy may comprise chest compressions for cardiac resuscitation, andthe first phase of the feedback cue may correspond to the upstroke ofthe rescuer's compression movement, and the second phase of feedback cuemay correspond to the downstroke of the rescuer's compression movement.The feedback cues may comprise audible sounds, and the first and secondphases may differ in one or both of frequency and amplitude. Thefeedback cues may comprise an upstroke cue for chest compression, andthe upstroke cue may vary in frequency, with the frequency increasing asthe rescuer's body rises during upstroke prior to delivery ofcompression. The feedback cues may further comprise a downstroke cuethat varies in frequency, with the frequency varying with time duringdelivery of compression. The downstroke cue may be shorter in durationthan the upstroke cue. The downstroke cue may grow in volume, withcrescendo at approximately the bottom of the delivered compression. Theprocessor may be configured to determine a latency between cue times andactual delivery times, and to use the latency and the desired deliverytimes in determining the cue times. The processor may be configured tomaintain a similar temporal relationship between cue times and actualdelivery times. The similar temporal relationship may have the cue timesoccur prior to the actual delivery times. The processor may beconfigured to use a tracking filter to predict actual delivery timesbased on the user's past performance in delivering the repetitivetherapy. The tracking filter may comprise a Kalman filter. The processormay be configured to compensate for a hysteresis relationship betweencue times and actual delivery times. The tracking filter may beconfigured to limit the influence of brief departures of actual deliverytimes from desired delivery times. A low pass filter may provide thelimit on influence of brief departures of delivery times. The desireddelivery times may be selected based on measured physiology of thepatient. The measured physiology may comprise the ECG of the patient.The desired delivery times may be times other than the T wave in theECG. The measured physiology may be PEA of the heart, and the desireddelivery times may be selected to produce actual chest compression timesphased relative to the PEA to improve hemodynamic output. The measuredphysiology may be low level mechanical activity of heart, and thedesired delivery times may be selected to produce actual chestcompression times phased relative to the low level mechanical activityto improve hemodynamic output. The measured physiology may comprise therhythm state of the heart, and the processor may be further configuredto vary cue times in accordance with at least some changes in rhythmstate. The rhythm state may be taken into account in deciding whether tophase feedback cues relative to the patient's underlying circulatoryactivity. The measured physiology may comprise the times of particularphysiological events and the cue times may be selected to produce adesired temporal relationship between the times of the physiologicalevents and the actual delivery times. The physiological events may bemechanical contractions of the heart. The desired delivery times may beshortly following pacing stimuli delivered to the heart, so that chestcompressions occur during periods of improved myocardial tone resultingfrom the pacing stimuli.

In a second aspect of the invention, the invention features a medicaldevice of the type used for assisting a user in manually deliveringrepetitive therapy to a patient, the device comprising a feedback deviceconfigured to generate feedback cues to assist the user in timing thedelivery of the repetitive therapy, wherein the repetitive therapycomprises psychomotor activity on the part of the user delivering thetherapy, and a processor, memory, and associated circuitry configured togenerate feedback cues with at least two phases, a first phasecorresponding to a first phase of the psychomotor activity and a secondphase corresponding to a second phase of the psychomotor activity.

In preferred implementations, one or more of the following features maybe incorporated. The manual repetitive therapy may be chest compressionin manual cardiac resuscitation, and the first phase of psychomotoractivity may comprise the upstroke movement by the user, and the secondphase of the psychomotor activity may comprise the downstroke movementby the user.

In a third implementation, the invention features a medical device forassisting a user in manually delivering chest compressions to a patientas part of cardiac resuscitation, the device comprising a feedbackdevice configured to generate feedback cues to assist the user in timingthe delivery of the chest compressions, a processor, memory, andassociated circuitry configured to determine cue times at which thefeedback cues are generated by the feedback device, wherein the feedbackcues have at least two phases distinguishable by the user, with a firstphase corresponding to an upstroke phase of the rescuer's movement, anda second phase of the feedback cue corresponding to a downstroke of therescuer's compression movement.

In preferred implementations, one or more of the following features maybe incorporated. The feedback cues may comprise audible sounds, and thefirst and second phases may differ in one or both of frequency andamplitude. The feedback cues may comprise an upstroke cue for chestcompression, and the upstroke cue may vary in frequency, with thefrequency increasing as the rescuer's body rises during upstroke priorto delivery of compression. The feedback cue may further comprise adownstroke cue that varies in frequency, with the frequency varying withtime during delivery of compression. The downstroke cue may be shorterin duration than the upstroke cue. The downstroke cue may grow involume, with crescendo at approximately the bottom of the deliveredcompression.

In a fourth aspect, the invention features a medical device forassisting a user in manually delivering chest compressions andventilations to a patient as part of cardiac resuscitation, the devicecomprising a feedback device configured to generate feedback cues toassist the user in timing the delivery of the chest compressions andventilations, a processor, memory, and associated circuitry configuredto determine cue times at which the feedback cues are generated by thefeedback device, wherein the feedback cues are auditory, and thefeedback cue for chest compressions is a different sound from thefeedback cue for ventilations.

In preferred implementations, one or more of the following features maybe incorporated. The difference in sound between the compression andventilation cues may be a difference in tone. The feedback cue tone forcompressions may overlap the feedback cue tone for ventilations on atleast some occasions. The ventilation feedback cue may be a graduallychanging sound with a duration that overlaps a plurality of compressionfeedback cues, which are of substantially shorter duration.

In a fifth aspect, the invention features a cardiac resuscitation deviceof the type used for assisting a user in manually delivering repetitivechest compressions to a patient, the device comprising a feedback deviceconfigured to generate non-verbal feedback cues to provide the user withfeedback to assist the user with respect to at least one compressionparameter, at least one sensor or circuit element configured todetermine the user's performance with respect to the compressionparameter, and a processor, memory, and associated circuitry configuredto compare the user's actual performance with respect to the compressionparameter to a desired performance with respect to the compressionparameter, and to determine non-verbal feedback cues to assist the userin achieving performance closer to the desired performance.

In preferred implementations, one or more of the following features maybe incorporated. The compression parameters may be one or a combinationof more than one of the following parameters: compression depth;compression velocity; duty cycle; velocity of chest release;intrathoracic pressure during compressions; pleural pressures duringcompressions; sternal position, velocity, or acceleration; chest wall orsternum strain or deformation. The processor may be configured with aphysiological model that relates delivery of the repetitive chestcompressions to the at least one compression parameter.

In a sixth aspect, the invention features a method of automaticallydelivering chest compressions in cardiac resuscitation, comprisingengaging the patient with a device for automatically delivering chestcompressions at compression delivery times, detecting the timing ofelectrical pacing stimuli being delivered to the patient, and selectingthe compression delivery times to be synchronized with a selected phaseof electrical pacing stimuli.

In preferred implementations, one or more of the following features maybe incorporated. The device delivering compressions may be separate fromdevice delivering electrical pacing. The device may detect the timing ofelectrical pacing stimuli from electrodes on the patient.

In a seventh aspect, the invention features a method of delivering asecond repetitive therapy while repetitive chest compressions are beingdelivered, comprising detecting the timing of the repetitive chestcompressions, and delivering the second repetitive therapy at times thatare synchronized to a selected phase of the repetitive chestcompressions.

In preferred implementations, one or more of the following features maybe incorporated. The second repetitive therapy may compriseelectromagnetic pacing stimuli. The pacing stimuli may be subthresholdand may be configured to improve tone of myocardium. The pacing stimulimay be above threshold and may be configured to produce cardiaccontractions. The chest compressions may be delivered manually. Thechest compressions may be delivered by an automatic device. The feedbackcues may be non-verbal cues.

In an eighth aspect, the invention features a medical device forassisting a user in manually delivering repetitive therapy to a patient,the device comprising a visual display for generating a visual,non-verbal feedback graphic to provide the user with a graphicalindication of how well the repetitive therapy is being delivered, atleast one sensor for sensing at least one parameter relating to how wellthe therapy is being delivered, and a processor, memory, and associatedcircuitry configured to process at least one output from at least onesensor to control the appearance of the graphical indication on thevisual display.

In preferred implementations, one or more of the following features maybe incorporated. The repetitive therapy may comprise chest compressionsas part of cardiac resuscitation. The repetitive therapy may compriseventilation as part of cardiac resuscitation. The graphical indicationmay comprise at least a first graphical element that provides the userwith feedback generally on a compression by compression basis as toapproximately how well individual compressions are being delivered. Thegraphical indication may further comprise at least a second graphicalelement that provides the user with feedback on an estimate of thecumulative impact of past compressions on coronary perfusion pressure.The first graphical element may be the color or other graphical aspectof the boundary of a bar element, and the second graphical element maybe the percentage area within the bar element that bears a color orother graphical aspect. There may be at least two sensors and at leasttwo parameters, and the graphical indication may comprise a firstgraphical element comprising a first indicator that moves along a firstaxis to convey a value of a first parameter and a second graphicalelement comprising a second indicator that moves along a second axisorthogonal to the first axis to convey a value of a second parameter.There may be at least three parameters, and the graphical indication mayfurther comprise a third graphical element positioned at theintersection of the first and second axes, the third graphical elementmay configured to convey a value of a third parameter.

In a ninth aspect, the invention features an ultrasonic sensor systemfor measuring blood flow, the sensor system comprising an ultrasonicprobe positioned at the end of a catheter, the probe and catheter beingconfigured to be inserted into or at the opening of the esophagus,wherein the probe is configured to deliver and measure ultrasonic soundenergy posteriorly toward the spine at approximately the cervicalvertebra C3-C6, measuring the reflected ultrasound energy from both thevertebrae and blood vessels; and a processor, memory, and associatedcircuitry configured to process the output of the probe to estimateblood flow in an artery or vein by ultrasonic Doppler flow measurement.

In preferred implementations, one or more of the following features maybe incorporated. The system may be combined with a cardiac resuscitationdevice and the estimated blood flow determined from the sensor outputmay be used in determining the timing of feedback cues delivered to theuser. The sensor may be configured to be inserted into the esophagus andto deliver ultrasonic energy toward the cervical vertebrae to estimateblood flow in the vertebral artery.

In a tenth aspect, the invention features an ultrasonic sensor systemfor measuring blood flow, the sensor system comprising an ultrasonicprobe positioned at the end of a catheter, the probe and catheter beingconfigured to be inserted into or at the opening of the esophagus,wherein the probe is conically shaped such that it seats against thebase of the pharynx at the superior end of the esophagus in the area ofthe esophageal muscle and the wide end of the probe just above that inthe lower pharynx, wherein the probe is configured to deliver andmeasure ultrasonic sound energy laterally, and wherein the sensor isconfigured to deliver ultrasonic energy in a beam directed at an upwardangle to intersect the common carotid artery and internal jugular vein;and a processor, memory, and associated circuitry configured to processthe output of the probe to estimate blood flow in an artery or vein byultrasonic Doppler flow measurement.

In preferred implementations, the processor may be configured to processthe output of the ultrasonic probe to estimate blood flow velocity forboth the carotid artery and jugular vein. The processor may beconfigured to calculate a pulsatile index as the difference of the peakaortic velocity and minimum diastolic velocity divided by the averagevelocity over one cardiac cycle. The processor may be configured tocalculate a resistance index as the difference between the peak aorticvelocity and minimum diastolic velocity divided by the peak aorticvelocity.

The invention has many advantages, including the following (some ofwhich may only be present in some aspects and some implementations):

Because the heart is in the early stages of recovery after adefibrillation shock, often with rhythmic electrical activity butdegraded mechanical output, cardiac recovery is enhanced by theinvention, for chest compressions are synchronized with the normal, iflow level, mechanical activity of the recovering heart. The inventionmay help provide effective CPR for patients in non-perfusing,fibrillatory rhythms as well as for patients in hemodynamically unstableor ineffective rhythms such as PEA.

The invention's ability to synchronize chest compression to the activityof a damaged heart may improve perfusion. Without the invention, chestcompressions may occur during ventricular filling, and thus be lesseffective, as the volume of blood in the heart is small and little or noblood is ejected in to the aorta and coronary arteries. A compressionduring this time may increase-intrathoracic and/or diastolic pressuresand further slow ventricular filling.

The invention may improve effectiveness of CPR during PEA because thecompressions can be timed to occur during specific phases of systolesuch as isovolumetric contraction.

Asystolic rhythms may convert spontaneously to PEA during the course ofCPR, and asynchronous delivery of chest compressions during PEA, as istypically currently performed, is substantially suboptimal with regardto circulatory hemodynamics. In these early stages of recovery, such asduring post-shock PEA, the heart is actually contracting to some degreeand asynchronous compression phasing may inflict additional stress onthe myocardium as well as lower ejection fractions. The inventionprovides detection of the change of the heart from one rhythm state tothe next and may provide feedback to the rescuer that synchronizes(entrains) the phase of the rescuer's CPR activities such as ventilationand chest compressions to the underlying electrical and mechanicalactivity of the heart and lungs. This has the advantages both ofreducing the need for interruptions of chest compressions as well asimproving hemodynamics.

The invention may provide feedback to a rescuer on a compression bycompression basis, thus monitoring the quality of CPR (e.g. depth ofcompressions) as well as looking at the specific effects of thecompression on the patient's heart. CPR guidelines necessarily cover thegeneral population, and individual parameters such as depth or rate ofcompressions may not be optimal for individual victims. Assessment ofindividual compressions may be beneficial to the rescuer both inproviding more effective CPR as well as conserving energy by notcompressing the chest with more force or speed than required.

The invention may adjust the timing of feedback cues to synchronize themwith actual compressions so that a rescuer does not get confused anddisoriented by cues occurring unexpectedly at what appear to be oddtimes. By measuring latency of a cue and the time of onset ofcompression, the cues may be timed so that a cue does not depart toomuch from the normal latency. For example, where rescuer is tiring, andthe rate of compressions is falling, the cue timing may be adjusted alittle to encourage the rescuer to increase the rate, but adjustmentsmay be done in such a way that only small changes in relative phase ofcompression and cue occur. In prior art, where only the rate of feedbackcues was addressed, the relative phase of the cue and the onset ofcompression could be all over the place, and cause disorientation.

Other features and advantages of the invention will be found in thedetailed description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a system block diagram.

FIG. 2 is a depiction of examples of compression timing relative tofeedback tone timing (changing from late to synchronized).

FIG. 3 is a detailed timing diagram showing the various parameters offeedback compression timing.

FIG. 4 is a block diagram showing a closed loop control system for phasesynchronization (entrainment) of rhythmic resuscitation actions.

FIG. 5 is a plot showing an example of a hysteretic relay operator.

FIGS. 6A and 6B are plots showing examples of a minor hysteresis loop.

FIG. 7 is a plot showing a variable phaser function.

FIG. 8 is a block diagram showing a hysteresis controller.

FIG. 9 is a flow chart showing a method of parameter-based CPR controlfeedback.

FIG. 10 is a block diagram of a resuscitation device, showing processingused to deliver audio prompts to a rescuer performing chestcompressions.

FIG. 11 is a block diagram of a resuscitation device, showing processingto provide advisory analysis with CPR feedback.

FIGS. 12A, 12B, and 12C are examples of graphical displays for providingfeedback to a rescuer on ventilation and compression.

FIGS. 13A, 13B, and 13C are diagrammatic views of an ultrasonic bloodflow sensor positioned in the superior end of the esophagus forproviding physiological feedback.

DETAILED DESCRIPTION

There are a great many different implementations of the inventionpossible, too many to possibly describe herein. Some possibleimplementations that are presently preferred are described below. Itcannot be emphasized too strongly, however, that these are descriptionsof implementations of the invention, and not descriptions of theinvention, which is not limited to the detailed implementationsdescribed in this section but is described in broader terms in theclaims.

Referring to FIG. 1, one or more sensors or instruments 1, 2, 3 are usedto acquire physiological signals from the patient. Pre-processing ofcertain signals may be required to derive relevant measurements orremove artifacts. For example, CPR artifact may be removed from the ECGsignal using known techniques. In one such technique, sensor 3 detectswhen a compression actually occurs. This sensor could be anaccelerometer located in a small plastic housing that resides underneatha rescuer's hands. Using signal processing methods (as disclosed inpending U.S. application Ser. No. 10/704,366, filed Nov. 6, 2003,entitled “Method and Apparatus for Enhancement of Chest CompressionsDuring CPR,” incorporated herein by reference), chest displacement isestimated by double integration of the acceleration signal. The time ofonset of a chest compression 29 can be determined from the estimateddisplacement. The time of onset of a chest compression can be determinedin other ways, including from transthoracic impedance, which istypically measured by AEDs, or from the artifact generated in the ECG bythe chest compression. A speaker 6 generates a feedback tone 21 (onepossible type of feedback cue), which we also refer to as thecompression rate tone (CRT), at the desired rate and timing with regardto the cardiac cycle.

As shown in FIG. 2, the algorithm corrects for the rescuer's timingerrors in performing chest compressions. In the example in FIG. 2, thealgorithm measures the latency 20 between the calculated targetcompression time, the feedback tone 21, and the actual compression 29.The algorithm advances the feedback tone (CRT) to correct for therescuer's latency 20 and detects when the compression is synchronizedwith the desired rate and phase of compressions (which has occurred bythe fourth compression in the figure).

Although auditory tones are preferred for feedback cues to the rescuer,other forms of feedback cues could be provided to the rescuer, includingvisual signals.

The algorithm to convert input signals to feedback tones (or othercompression feedback cues) may vary in complexity. The algorithm may beas simple as detecting a QRS complex or other point of interest in theECG signal. Or it may involve more complex methods, including predictivetracking algorithms such as a Kalman filter or other methods using pastreadings to predict when the next compression should take place. Thepredicted time for a compression may be used to immediately update thetime at which a feedback cue is delivered, or (as is shown in FIG. 2),the timing of the feedback cue may be adjusted slowly over multiplecompressions, allowing a rescuer to slowly change the rhythm ofcompressions to bring compressions to the desired timing.

Referring to FIG. 3, in some implementations the feedback cue may havetwo phases (more than two phases are also possible). Using two phasesaddresses our discovery that the act of delivering chest compressions isprimarily a biphasic psychomotor activity, with the rescuer's posteriormuscle groups such as the erector spinalis and gluteus maximus involvedin the preparatory upstroke phase of the compression cycle, and theanterior internal and external oblique muscle groups involved in thedownstroke. A single-phase tone has the difficulty that it correspondsto the downstroke in the rescuer's mind, but significant preparatoryactivity is required before the downstroke can be delivered (i.e., theupstroke before the compression), and so the rescuer is required toanticipate when the next compression tone is going to occur so that hisdownstroke coincides with the single phase tone. This difficulty isbelieved to be the primary reason that compression tones as currentlyimplemented in various CPR prompting devices are not as effective asthey could be.

In some implementations, the frequency and volume of the feedback toneis varied between the phases (upstroke and downstroke). Frequency isused as an aural metaphor for the height of the rescuer's upper bodyfrom the victim, e.g., a tone that ramps up in frequency indicates theupstroke. In some implementations, the upstroke phase tone (UPT) 38lasts for the amount of time that the rescuer performs the upstroke,making it possible for the rescuer to accurately follow the non-verbalinstruction provided by the tone, and be properly positioned to beginthe downstroke when the downstroke phase of the tone (DPT) 39 occurs. Insome implementations, the DPT 39 is a shorter duration tone that rampsdown in frequency fairly quickly, with the a crescendo in volume as thefrequency decreases and with a maximum volume occurring at the pointthat would correspond to the bottom of the compression downstroke.

In other implementations, this approach can be applied to othermultiphasic repetitive psychomotor activities, including ones with morethan two phases, by providing a multiphasic tone whose phases areclearly delineated to the rescuer and for which the parameters of eachphase of the tone are adjusted to assist synchronization of each phaseof the psychomotor activity. Other possibilities for parameters of thefeedback tone for each phase are bandwidth of a colored-noise signal orthe volume envelope of a signal. For example, increasing the ramp rateof the envelope attack can be used to indicate to rescuers that theyshould increase the velocity of the downstroke of the compression.

A block diagram for one possible control system for generating thefeedback tones is shown in FIG. 4. A timing diagram is shown in FIG. 3.The control algorithm adjusts a feedback control vector F(kT+1) 63 (thevector includes UPT onset, φ, σ, Δf⁺, Δf⁻, ε_(onset), ε_(hold), ε_(off))to minimize an error signal e(kT) 52, where T is the present sampleinterval. An input signal x(kT) 64 is the desired phase and rate for thecompressions. X(kT) 64 may take the form of discrete times at whichcompressions are desired to occur, t₀, t₀+1/f, t₀+2/f . . . , withcorresponding times at which a compression was actually detected by themotion detection algorithm (which integrates 54 the output of anaccelerometer sensor 18). X(kT) 64 may also take the form of a waveformvector describing the desired motion parameters of the rescuer'sactions. The motion feedback signal preferably takes the form of a setof waveforms, vector V′(kT) 57, composed of the estimates of actualacceleration, velocity, and displacement waveforms. The error signal,e(kT) 52 is the difference of between V′(kT) 57 and the desired motion,x(kT) 64. Estimates, H′(kT) 56 are also made of the patient'sphysiological status, particularly the hemodynamic state as measured bysuch parameters as ECG 1, pulse oximetry 2, invasive blood pressure, andnon-invasive blood pressure. H′(kT) 56 is fed back to adjust X(kT) 64 sothat the rate and phase of X(kT) 64 are optimized to provide maximalbenefit to the patient's current physiological state.

Referring also to FIGS. 3 and 4, there are several intervals that arecalculated within the Feedback Controller Module (FCM) 53. Parameter δ(25) is the time difference between the end of UPT 38 and the time atwhich the compression actually occurred. Parameter ψ (24) is the timedifference between the actual compression and the point in time thecompression was desired to occur, t₀ (30). The desired compression timet₀ may correspond be a particular fiducial on an ECG 1 or pulse oximetry2 waveform corresponding to the systolic phase of the cardiac cycle.Parameter φ (31) is the time difference between the end of UPT 38 andthe onset of the DPT 39, and quantifies a natural anticipatory pauseprior to the onset of DPT 39 and the action of compression downstroke bythe rescuer. Parameter σ is the slope of frequency increase, measured inunits of Hz/millisecond; (1/σ*Δf) is the length of time required for theUPT ramp, where Δf (28) is the total change in frequency during the UPT38 phase.

The object of the closed loop control system of FIG. 4 is to reduce ψ,so that the actual compression occurs near the time of the desiredcompression time 30. If, however, the UPT 38 is too far out of phasewith the rescuer's actual compressions, they will become confused andtheir performance will be adversely impacted. To provide a way or slowlyadjusting the relative phase of the UPT and the rescuer's actualcompressions, a moving factor, β, may be used, such that,ψ_(t+1)=ψ_(t)*β.β may be a variable whose value is adjusted using conventional controlsystem methods known to those skilled in the art such as proportional,difference, integral (PID), state space, or non-linear control methods.In the case where the underlying rhythm of the patient is asystole (noheart rhythm to synchronize the compressions to) and the system is onlytrying to cause the rescuer to deliver compressions at the correct rate,then t₀ will not correspond to a physiological fiducial.

Feedback controller 53 (FIG. 4) will often have a low pass or medianfilter to minimize spurious error signals that may result, for example,from the rescuer's brief departures from delivery of well-timedcompressions. The filter can be configured to switch its bandwidthdepending on the state of the system. For instance, as is known intracking systems, the filter's bandwidth may be initially set wide toacquire synchronization. But once the synchronization is acquired (therescuer is regularly delivering compressions at an acceptable timingerror relative to the desired compression times), the bandwidth may beswitched to a more narrow setting to minimize the effects of short termtiming errors by the rescuer. In other words, the state of the feedbacksystem could change from “bad” compressions to “good” compressions basedon the ratio ψ/P being less than 0.2 for more than three compressions(ψ/P is a normalized measure of how much error between the desired timefor a compression and the actual compression is tolerable). The filtershould also be configured to detect loss of synchronization—e.g., bylooking for either a sudden or consistent increase in either the mean orstandard deviation of ψ/P.

The governing equation of the process is constructed such that thecompression period, P (32), is fixed and an estimation of the futureinterval, δ_(t+1), is calculated to determine when the next UPT onsetshould occur:UPT onset=t _(0+{) P−[φ−(δ_(t+1)−ψ_(t+1))+(1/σ*Δf)]}

Tracking algorithms such as the Kalman filter may be used for theestimation and prediction of (δ_(t+1)−ψ_(t+1)). The Kalman filterestimates a process by using a form of feedback control; the filterestimates the process state at some time and then obtains feedback inthe form of (noisy) measurements. As such, the equations for the Kalmanfilter fall into two groups: time update equations and measurementupdate equations. The time update equations are responsible forprojecting forward (in time) the current state and error covarianceestimates to obtain the a priori estimates for the next time step. Themeasurement update equations are responsible for the feedback—i.e. forincorporating a new measurement into the a priori estimate to obtain animproved a posteriori estimate. The time update equations can also bethought of as predictor equations, while the measurement updateequations can be thought of as corrector equations. Indeed the finalestimation algorithm resembles that of a predictor-corrector algorithmfor solving numerical problems.

Discrete Kalman filter time update equations:{circumflex over (x)} _(k) ⁻ =A{circumflex over (x)} _(k−1) +Bu _(k−1)P _(k) ⁻ =AP _(k−1) A ^(T) +QDiscrete Kalman filter measurement update equations:K _(k) =P _(k) ⁻ H ^(T)(HP _(k) ⁻ H ^(T) +R)⁻¹{circumflex over (x)} _(k) ={circumflex over (x)} _(k) ⁻ +K _(k)(z _(k)−H{circumflex over (x)} _(k) ⁻)P _(k)=(I−K _(k) H)P _(k) ⁻

The first task during the measurement update is to compute the Kalmangain, K_(k), The next step is to actually measure the process to obtain,and then to generate an a posteriori state estimate by incorporating themeasurement, z_(k). The final step is to obtain an a posteriori errorcovariance estimate, P_(k). After each time and measurement update pair,the process is repeated with the previous a posteriori estimates used toproject or predict the new a priori estimates. This recursive nature isone of the very appealing features of the Kalman filter—it makespractical implementations much more feasible than (for example) animplementation of a Wiener filter which is designed to operate on all ofthe data directly for each estimate. The Kalman filter insteadrecursively conditions the current estimate on all of the pastmeasurements. The equation,{circumflex over (x)} _(k) ={circumflex over (x)} _(k) ⁻ +K _(k)(z _(k)−H{circumflex over (x)} _(k) ⁻is termed the predictor equation.

One of the primary limitations of the Kalman filter is that it onlymodels a linear system with Gaussian distribution, not often encounteredin the physiological setting. The best known algorithm to solve theproblem of non-Gaussian, nonlinear filtering is the extended Kalmanfilter (EKF). This filter is based upon the principle of linearizing themeasurements and evolution models using Taylor series expansions. Theseries approximations in the EKF algorithm can, however, lead to poorrepresentations of the nonlinear functions and probability distributionsof interest. As a result, this filter can diverge. Based on thehypothesis that it is easier to approximate a Gaussian distribution thanit is to approximate arbitrary nonlinear functions other researchershave developed a filter termed the unscented Kalman filter (UKF). It hasbeen shown that the UKF leads to more accurate results than the EKF andthat in particular it generates much better estimates of the covarianceof the states (the EKF often seems to underestimate this quantity). TheUKF has, however, the limitation that it does not apply to generalnon-Gaussian distributions as is often the case with the ECG spectraldistributions. Sequential Monte Carlo methods, also known as particlefilters overcome this limitation and allow for a complete representationof the posterior distribution of the states, so that any statisticalestimates, such as the mean, modes, kurtosis and variance, can be easilycomputed. Particle Filters can therefore, deal with any nonlinearitiesor distributions. Particle filters rely on importance sampling and, as aresult, require the design of proposal distributions that canapproximate the posterior distribution reasonably well. In general, itis hard to design such proposals. The most common strategy is to samplefrom the probabilistic model of the states evolution (transition prior).This strategy can, however, fail if the new measurements appear in thetail of the prior or if the likelihood is too peaked in comparison tothe prior.

Some implementations use a estimator/predictor trajectory trackingtechnique known as the Unscented Particle Filter (UPF) as developed byMerwe, Doucet, Freitasz and Wan. Pseudocode for the UPF is as follows:

Unscented Particle Filter:

Initialization: t=0.

For i=1, . . . N, draw states (particles) x₀ ^((i)) from the prior p(x₀)and set,

${\overset{\_}{x}}_{0}^{(i)} = {E\left\lbrack x_{0}^{(i)} \right\rbrack}$$P_{0}^{(i)} = {E\left\lbrack {\left( {x_{0}^{(i)} - {\overset{\_}{x}}_{0}^{(i)}} \right)\left( {x_{0}^{(i)} - {\overset{\_}{x}}_{0}^{(i)}} \right)^{T}} \right\rbrack}$${\overset{\_}{x}}_{0}^{{(i)}a} = {{E\left\lbrack x^{{(i)}a} \right\rbrack} = \left\lbrack {\left( {\overset{\_}{x}}_{0}^{(i)} \right)^{T}\mspace{14mu} 0\mspace{14mu} 0} \right\rbrack^{T}}$$P_{0}^{{(i)}a} = {{E\left\lbrack {\left( {x_{0}^{{(i)}a} - {\overset{\_}{x}}_{0}^{{(i)}a}} \right)\left( {x_{0}^{{(i)}a} - {\overset{\_}{x}}_{0}^{{(i)}a}} \right)^{T}} \right\rbrack} = \begin{bmatrix}P_{0}^{(i)} & 0 & 0 \\0 & Q & 0 \\0 & 0 & R\end{bmatrix}}$

For t=1, 2, . . . ,

-   -   a) Importance sampling step:        -   For i=1, . . . N: Update particles with the UKF:        -   Calculate sigma points:

$\chi_{i - 1}^{{(i)}a} = \left\lbrack {{{\overset{\_}{x}}_{t - 1}^{{(i)}a}{\overset{\_}{x}}_{t - 1}^{{(i)}a}} \pm \sqrt{\left( {n_{a} + \lambda} \right)P_{t - 1}^{{(i)}a}}} \right\rbrack$

-   -   -   Predict future particle (time update)

$\chi_{i❘{t - 1}}^{{(i)}x} = {{{f\left( {\chi_{t - 1}^{{(i)}x},\chi_{t - 1}^{{(i)}v}} \right)}\mspace{14mu}{\overset{\_}{x}}_{t❘{i - 1}}^{(i)}} = {\sum\limits_{j = 0}^{2n_{a}}{W_{j}^{(m)}\chi_{j,{t❘{t - 1}}}^{{(i)}x}}}}$$P_{i❘{t - 1}}^{(i)} = {\sum\limits_{j = 0}^{2n_{a}}{{W_{j}^{(c)}\left\lbrack {\chi_{j,{t❘{t - 1}}}^{{(i)}x} - {\overset{\_}{x}}_{i❘{t - 1}}^{(i)}} \right\rbrack}\left\lbrack {\chi_{j,{t❘{t - 1}}}^{{(i)}x} - {\overset{\_}{x}}_{t❘{t - 1}}^{(i)}} \right\rbrack}^{T}}$$y_{i❘{t - 1}}^{(i)} = {{{h\left( {\chi_{t❘{t - 1}}^{{(i)}x},\chi_{t - 1}^{{(i)}n}} \right)}\mspace{14mu}{\overset{\_}{y}}_{t❘{t - 1}}^{(i)}} = {\sum\limits_{j = 0}^{2n_{a}}{W_{j}^{(m)}y_{j,{t❘{t - 1}}}^{(i)}}}}$

-   -   -   Incorporate new observation (measurement update)

$P_{{\overset{\_}{y}}_{t}{\overset{\_}{y}}_{t}} = {\sum\limits_{j = 0}^{2n_{a}}{{W_{j}^{(c)}\left\lbrack {y_{j,{t❘{t - 1}}}^{(i)} - {\overset{\_}{y}}_{t❘{t - 1}}^{(i)}} \right\rbrack}\left\lbrack {y_{j,{t❘{t - 1}}}^{(i)} - {\overset{\_}{y}}_{t❘{t - 1}}^{(i)}} \right\rbrack}^{T}}$$P_{x_{t}y_{t}} = {\sum\limits_{j = 0}^{2n_{a}}{{W_{j}^{(c)}\left\lbrack {\chi_{j,{t❘{t - 1}}}^{(i)} - {\overset{\_}{x}}_{t❘{t - 1}}^{(i)}} \right\rbrack}\left\lbrack {y_{j,{t❘{t - 1}}}^{(i)} - {\overset{\_}{y}}_{t❘{t - 1}}^{(i)}} \right\rbrack}^{T}}$$K_{t} = {{P_{x_{t}y_{t}}P_{{\overset{\_}{y}}_{t}{\overset{\_}{y}}_{t}}^{- 1}\mspace{14mu}{\overset{\_}{x}}_{t❘{t - 1}}^{(i)}} + {K_{t}\left( {y_{t} - {\overset{\_}{y}}_{t❘{t - 1}}^{(i)}} \right)}}$${\hat{P}}_{t}^{(i)} = {P_{t❘{t - 1}}^{(i)} - {K_{t}P_{{\overset{\_}{y}}_{t}{\overset{\_}{y}}_{t}}K_{t}^{T}}}$${{Sample}\mspace{14mu}{\left. {\hat{x}}_{t}^{(i)} \right.\sim{q\left( {{x_{t}^{(i)}❘x_{0:{t - 1}}^{(i)}},,y_{1:t}} \right)}}} = {{??}\left( {{\overset{\_}{x}}_{t}^{(i)},{\hat{P}}_{t}^{(i)}} \right)}$${{Set}\mspace{14mu}{\hat{x}}_{0:t}^{(i)}}\overset{\Delta}{=}{\left( {x_{0:{t - 1}}^{(i)},{\hat{x}}_{t}^{(i)}} \right)\mspace{14mu}{and}\mspace{14mu}{{{\hat{P}}_{0:t}^{(i)}\left( {P_{0:{t - 1}}^{(i)},{\hat{P}}_{t}^{(i)}} \right)}.}}$

-   -   -   For i=1, . . . N, evaluate the importance weights up to a            normalizing constant:

$w_{t}^{(i)} \propto \frac{{p\left( {y_{t}❘{\hat{x}}_{t}^{(i)}} \right)}{p\left( {{\hat{x}}_{t}^{(i)}❘x_{t - 1}^{(i)}} \right)}}{q\left( {{{\hat{x}}_{t}^{(i)}❘x_{0:{t - 1}}^{(i)}},y_{1:t}} \right)}$

-   -   -   For i=1, . . . N, normalize the importance weights.

    -   b) Selection Step        -   Multiply/Suppress particles,            ({circumflex over (x)} _(0:t) ^((i)) ,{circumflex over (P)}            _(0:t) ^((i)))        -   with high/low importance weights,            {tilde over (w)} _(t) ^((i))        -   respectively, to obtain N random particles.

    -   c) Output: The output of the algorithm is a set of samples that        can be used to approximate the posterior distribution as        follows:

${{p\left( {x_{0:t}❘y_{1:t}} \right)} \approx {\hat{p}\left( {x_{0:t}❘y_{1:t}} \right)}} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\delta_{(x_{0:t}^{(i)})}\left( {\mathbb{d}x_{0:t}} \right)}}}$

-   -   -   Resulting in the estimate of,

${E\left( {g_{t}\left( x_{0:i} \right)} \right)} = {{\int{{g_{t}\left( x_{0:t} \right)}{p\left( {x_{0:i}❘y_{1:t}} \right)}{\mathbb{d}x_{0:i}}}} \approx {\frac{1}{N}{\sum\limits_{i = 1}^{N}{g_{t}\left( x_{0:i}^{(i)} \right)}}}}$for some function of interest, g_(t), for instance the marginalconditional mean or the marginal conditional covariance or other moment.

It has been shown in numerous studies on the psychology of perception aswell as usability testing of user interfaces that users have a poorability to quantify short durations of time, but are excellent atdiscerning temporal order, i.e., whether or not the compression feedbackoccurred before or after the actual compression. It is thus typicallyadvantageous that the delay, δ (25), always be positive, since smallabsolute shifts of δ that cause it to oscillate about zero can result inlarger adverse oscillations in the phase alignment of the rescuercompressions.

This inability of the rescuer to discern small changes in time durationmeans that there is, in effect, a dead band relationship between thedesired and actual compression timing. Within this dead band, a changein the timing of a feedback cue may not produce a change in the user'sperception of the desired timing. Such dead bands produce what iscommonly referred to as hysteresis. Hysteresis—the influence of theprevious history or treatment of a body on its subsequent response to agiven force or changed condition—is widely found in nature. It was firstrecognized in ferromagnetic materials, and subsequently in plasticity,friction, and phase transitions, as well as in somewhat different fieldssuch as mechanics, thermodynamics, biology, chemistry, and economics,among others. Hysteresis is present when the transfer function of thesystem changes depending on whether the input to the system isincreasing or decreasing.

Referring to FIGS. 5, 6A and 6B, the Preisach Model is often used torepresent hysteresis with non-local memory, i.e., the future values ofthe output y(t), for t>t₀, depend not only on y(t₀), but also on pastextrema of the input. The Preisach Model, in particular, considers aninfinite set of relay operators γ_(αβ) 80, where α81 and β82 correspondto the ascending and descending switching values where the outputswitches between −1 and +1.

In a restricted frequency range, it is possible to consider thathysteresis is rate independent and acts as an additive disturbance onthe linear dynamics of the system. Here, a system with hysteresis isseen as a parallel connection of a linear dynamical system with a rateindependent hysteresis with memory. In operator form the system can berepresented by:y=L[u]+{circumflex over (Γ)}[u]

where {circumflex over (Γ)} represents the rate independent hysteresiswith memory and L represents the dynamics of the system. The weightedresponse of an infinite collection of relays is summed over all possibleswitching values:

y(t) = Γ̂[u(t)] = ∫∫_(H)μ_(α, β)γ̂_(αβ)[u(t)]𝕕α𝕕β

FIGS. 6A and 6B show a minor hysteresis loop created after an inputsignal is varied between α₁ 83 and β₁ 84. The triangle T(α₁, β₁) isadded to the positive set S⁺ and subtracted from the negative set S⁻when the input reaches α₁, and subtracted from S⁺ when the input reachesβ₁ 84. When the input is at α₁ 83 the interface line L(t) is just a lineparallel to the β axis, creating a set of past extrema with one cornerat the intersection α₁ 83 and β₀ 85. When the input is at β₁ 84,triangle T(α₁,β₁) is added to the negative set S⁻, and the interfaceline L(t) is a step as shown in FIGS. 6A and 6B. This difference is whatcauses the loop to trace two different curves.

Referring to FIGS. 5-8, the Hysteresis Controller 90 is two-staged: ituses the “phaser” operator 92 that shifts its periodic input signal by aconstant phase angle for the first stage, and a variable phaser 91,shown in FIG. 7, for the second stage, governed by the equation:

${\upsilon(t)} = \left\{ \begin{matrix}{{{\cos\left( \phi_{1} \right)}{r(t)}} + {\frac{\sin\left( \phi_{1} \right)}{\omega}{\overset{.}{r}(t)}}} & {{{if}\mspace{14mu}{r}} \leq s} \\{{{\cos\left( \phi_{2} \right)}{r(t)}} + {\frac{\sin\left( \phi_{2} \right)}{\omega}{\overset{.}{r}(t)}}} & {{{if}\mspace{14mu}{r}} > s}\end{matrix} \right.$

where φ₁>=0 and φ₂<=0, and s 93 is empirically determined. Anapproximation of the discontinuous function depicted by the bold, solidlines in FIG. 7 is sometimes employed using the hyperbolic tangentfunction as shown by the light, solid line.

The feedback provided the rescuer 7 may be implemented in a variety offorms, including as visual and auditory cues (which are believed to bemost effective).

Various types of information on the patient's physiology may be used asinput to determine the timing of the feedback. For example, any of thefollowing physiological signals, or combinations of physiologicalsignals, could be used: ECG; measures of cardiac output; measures ofheart rate; blood pressure(s); oxygen saturation (SpO₂); heart sounds(including phonocardiography); heart imaging (including ultrasound);impedance cardiography.

The feedback cues could address a variety of compression parametersother than compression timing, including, for example, any of thefollowing, or combinations of the following: compression velocity;compression depth; duty cycle; velocity of chest release; intrathoracicpressures during compressions; pleural pressures during compressions;sternal position, velocity or acceleration; chest wall or sternal strainor deformation.

In some implementations, the quality of the chest compressions ismonitored, and the feedback cues varied to improve quality. For instancethe following compression parameters have been shown to have significanteffect on the hemodynamic effects of chest compressions: the depth ofthe compression, the velocity of the compression downstroke (improvingthe ejection fraction and systolic effectiveness), and achievement of arapid and complete release of pressure from the sternum during theupstroke of compression (thereby improving diastolic filling of theheart). By varying the feedback cues, it is possible to improve both thetiming and quality of compressions. The proper compression depth isspecified by AHA recommendations at 2 inches. It has been shown inanimal and theoretical models that the velocity of compression and fullrelease of pressure from the sternum may be equally important to depthof compression. In some implementations, the system may increase thefrequency variation Δf (28) during the upstroke cue (UPT) 38, with theresult that the rescuer will further release his hands from thepatient's sternum during the decompression phase. In someimplementations, increasing the audio volume of the downstroke cue (DPT)39 and the amplitude envelope may cause a rescuer to increase thevelocity of the compression downstroke. Also, the duty cycle of CPRcompressions (i.e., the percentage of time devoted to upstroke versusdownstroke) may be varied (e.g., in response to measured activity of theheart) by adjusting the relative ratio of time devoted to the UPT andDPT cues (e.g., lengthening the time devoted to the DPT cue may achievea longer downstroke by the rescuer).

In other implementations, feedback may be provided to the rescuer fortiming delivery of ventilation. This may be helpful in preventingover-ventilation as well as controlling intrathoracic pressures.Pressures from chest compressions and ventilations are an importantfactor in assisting venous return of blood to the heart as well asejecting blood from the ventricles. The currently recommended ratiobetween compressions and ventilations is 15:2 for adults. Like thecompression cycle, a ventilation cycle using a Bag Valve Mask (BVM) canbe represented to the rescuer as a biphasic sequence composed of thephase of squeezing the bag and the phase of releasing the bag. Thebiphasic audio tone for ventilation is distinct from that used forcompressions. This can be accomplished by making the respective feedbacktones for compressions and ventilation recognizable and distinct,preferably from a perceptually classifiable perspective. For instance,the tone for compressions might have the waveshape and harmonics suchthat it is perceived as a trumpet while the ventilation might have thewaveshape and harmonics such that it is perceived as a violin. Usingtechniques common to sound synthesis the fundamental frequency may beshifted for each of the tones to provide the change in frequencynecessary for feedback.

In other implementations, wherein an automated chest compression deviceand/or automated ventilator is available during a rescue, the automatedchest compressions and/or ventilation delivered by the automated devicemay be synchronized with the cardiac activity induced by repetitivecardiac stimulation therapy such a electrical pacing. Pacing can also beinduced by magnetic stimulation (U.S. Pat. Nos. 4,994,015 and 5,078,674)or mechanically induced stimulation using ultrasonic transducers. Theinduced hemodynamic response of the heart will vary from patient topatient and it is desirable that the mechanical compression delivered bythe automated chest compression device be synchronized to the inducedhemodynamic response in order to maximize blood flow and reduce energyconsumption of the myocardial tissue. The start time of the compressionpulse, t_(c), is also adjusted relative to the start time of the pacing,t_(p), such that t_(p)−t_(c)=κ−π, where κ (36) is the delay from thestart of a compression to the hemodynamic response and π (22) is thedelay from the start of a pacing pulse to the hemodynamic response. Asthe patient condition changes during the course of a reuscitation, thevalues of π and κ will change as drugs such epinephrine and amiodaroneare delivered which have effects on vascular tone and calcium andbeta-channel-related excitation-contraction (EC) coupling dynamics. Aswith the embodiment for manual compressions, a predictive algorithmwhich is used for the control of a mechanical compression device orinflatable vest can be used to take into account the changes in theresponse of the patient, with the results of the predictive algorithmapplied to timing of compressions applied by the device. Synchronizationmay be achieved either through direct communication between devices suchas a serial Universal Serial Bus (USB) interface or wirelessly using alow-latency wireless protocol such as the so-called ZigBee, IEEE802.15.4 protocol standard.

Pacing may also be combined, in some implementations, with manualcompressions as a means of augmenting the rescuer's mechanicalcompressions with the electrically-induced contractions of themyocardium. In these implementations, π may be adjusted relative to δsuch that the hemodynamic response of the electrically-induced activityslightly preceeds that induced by the manual compression by the rescuer,on the order of 50-100 milliseconds. During a resuscitation, the heartis in a state of profound ischemia resulting in a flacidity and loss oftone as lactate builds up in the myocardium and the tissue pH drops. Asa result of the loss of tone, the heart becomes a less-effective pumpstructure for generating blood flow during manual chest compressions.Drugs such as epinephrine act to improve tone, but because they aredelivered venously, their action may take 2-3 minutes during cardiacarrest, when the only blood flow is that induced by the chestcompressions. Pacing that may or may not be sufficient to actually causea satisfactory hemodynamic response as a result of the metabolicallycompromised state of the myocardium can sufficiently improve the tone ofthe myocardium immediately prior to, and synchronized with, themechanical compression without the therapeutic delay experienced withdrugs such as epinephrine. This instantaneous improvement in myocardialtone can substantially improve the hemodynamic effectiveness of themechanical compression.

In other implementations, feedback of the various parameters related tothe therapeutic interventions such as compressions and ventilations arefed back to the rescuer based on both the state of the patient and thequality of the compressions. In some simpler implementations, the systemprovides feedback in such a manner as to prevent the rescuer fromdelivering chest compressions during specific physiological events suchas T waves in the ECG which indicate ventricular repolarization. If acompression is delivered during a T wave, the compression may besubstantially more likely to induce life-threatening ventricularfibrillation, a process known as commotio cordis. In other and morerobust implementations, medical knowledge such as that just mentioned iscombined with a mathematical description of the circulatory system, suchas that described in Crit Care Med 2000 Vol. 28, No. 11 (Suppl.). As theauthor describes, the system of differential equations has beendescribed in a number of publications. In this specific instance, “thehuman circulation is represented by seven compliant chambers, connectedby resistances through which blood may flow. The compliances correspondto the thoracic aorta, abdominal aorta, superior vena cava and rightheart, abdominal and lower extremity veins, carotid arteries, andjugular veins. In addition, the chest compartment contains a pumprepresenting the pulmonary vascular and left heart compliances. Thispump may be configured to function either as a heart-like cardiac pump,in which applied pressure squeezes blood from the heart itself throughthe aortic valve, or as a global thoracic pressure pump, in whichapplied pressure squeezes blood from the pulmonary vascular bed, throughthe left heart, and into the periphery. Values for physiologic variablesdescribing a textbook normal “70-kg man” are used to specify compliancesand resistances in the model. The distribution of vascular conductances(1/resistances) into cranial, thoracic, and caudal components reflectstextbook distributions of cardiac output to various body regions.”

Referring to FIG. 4, a closed loop feedback method may be employed,using state space methods with the system estimation block 55 providedby a physiological model as the author describes above. The FeedbackController 53 may employ such traditional control system methods aproportional, difference, integral (PID), or state feedback controlmethods, e.g., as known to those skilled in the art. As an alternativeto the closed-loop control, the device may “search” for the bestcompression parameters by monitoring sensors as illustrated in theflowchart of FIG. 9. Although the flowchart shows only a singleparameter, multiple parameters may be varied while a sensor(s) monitorthe patient. The method varies parameters one at a time or in paralleland attempts to improve perfusion. The system may find that a value of acertain parameter (e.g., duty cycle) is producing improved perfusion,and continue therapy at that value, or continue to vary the parameter ina range near that value in case conditions change. Optimized searchmethods such as gradient steepest descent, self-annealing or geneticalgorithms may also be employed.

A steepest descent algorithm works by increasing a particular parameter(e.g., rate) and seeing if it results in some measured improvement inperformance of the system (e.g., EtCO2 values). If so then thatparticular parameter is further adjusted until the desired performanceof the system is achieved. In a two-parameter system (e.g. rate anddepth), it is viewed topographically, with the x-y coordinates beingvalues of the two parameters and the z-axis representing the systemperformance (EtCO2). Typically, the algorithms work to minimize someoutput value (hence steepest descent). In some implementations, theobjective would be trying to maximize the EtCO2 value. The method istypically entirely empirical, based on changing the parameter values andthen measuring the system output. At any point in time, the rescuer'srate and depth are located at a particular point on the topographic map.Adjusting each parameter separately will provide a gradient (local)slope. Then, assuming a monotonic slope over a sufficient region toencompass the desired EtCO2 value, the two parameters are both adjustedto achieve the desired EtCO2 value.

Synchronizing chest compressions with underlying physiological activitymay also supplement slow or bradycardic rhythms by timing compressionsto occur during ventricular diastole. E.g., a patient with a rhythm of30 beats per minute may receive better perfusion with chest compressionsdelivered between beats, making the effective heart rate more like 60beats per minute. Feedback is required for the rescuer to time thecompressions with some volume of blood in the ventricles and to avoidcompressing on T waves.

Referring to FIG. 10, in other implementations, a microphone 100 orother transducer may be used to detect heart sounds. These sounds may beamplified through the speaker 6 for a trained rescuer 7 or processed toprovide cues for a rescuer 7 with less training. Heart sounds may beused independently of other measures to determine CPR rates, depth,and/or duty cycle as well as to assess the effectiveness of CPR. Thismay be effective for a patient 11 in asystole where ECG 1, bloodpressures, or pulse do not suggest a natural rate or time for the heartto be compressed. The velocity of the DPT 39 phase of the compressionmay be adjusted to minimize valvular regurgitation. Adequate depth ofcompression may be assessed by a heart sound indicating valve closure.

In other implementations, compression timing and rate may be adjustedbased on any heart sound, although S₁ may be ideal since it indicatesthe start of ventricular systole. Over ventilation is estimated by theanalysis of S₂ since splitting of the aortic and pulmonary valveclosures increases with reduced intrathoracic pressure. Murmurs andother sounds may provide diagnostic information about damage to theheart and CPR parameters may be adjusted based on this information.

Other means such as ultrasound or transthoracic impedance can be used todetect and measure cardiac volume changes or blood flow. In someimplementations, a catheter is inserted into the patient's esophaguswith an ultrasonic probe at the distal end prior to intubation of thepatient's airway. The ultrasonic probe faces posteriorly towards thecervical vertebrae and is positioned at approximately the cervicalvertebra C3-C6, with the sound energy reflected off the vertebrae andproviding the sensor in the probe with a robust signal for measuringblow flow in the vertebral artery by ultrasonic doppler flow measurementmethods commonly in use. The benefits of such a system are several: (1)the transducer is positioned outside of the field where chestcompressions are occurring, thus minimizing the motion artifact induced;(2) the method provides an excellent method of measuring blood flow tothe brain; and (3) brain perfusion pressure (BPP) sufficient to induceeffective flows to the brain are harder to achieve with CPR chestcompressions than the coronary perfusion pressure (CPP) necessary toinduce effective perfusion of the heart, thus the vertebral flowmeasurement is a sensitive indicator of both effective BPP and CPPduring resuscitation efforts.

The vertebral arteries travel along the spinal column and cannot be feltfrom the outside. They join to form a single basilar artery near thebrain stem at the base of the skull. The arteries supply blood to theparietal and occipital lobes of the cerebrum, part of the cerebellum,and the brain stem. The parietal lobes contain the primary sensorycortex, which controls sensation (touch and pressure), and a largeassociation area that controls fine sensation (judgment of texture,weight, size, and shape). Damage to the right parietal lobe can causevisuo-spacial deficits, making it hard for the patient to find his/herway around new or even familiar places. Damage to the left parietal lobemay disrupt a patient's ability to understand spoken and/or writtenlanguage. The occipital lobe processes visual information. It is mainlyresponsible for visual reception and contains association areas thathelp in the visual recognition of shapes and colors. Damage to this lobecan cause visual deficits. The cerebellum is the second largest area ofthe brain. It controls reflexes, balance and certain aspects of movementand coordination. The brain stem is responsible for a variety ofautomatic functions that are critical to life, such as breathing,digestion and heart beat—as well as alertness and arousal (the state ofbeing awake). Thus, other implementations may monitor blood flow in thevertebral artery during resuscitation and adjust therapeuticinterventions to maximize that flow.

Referring to FIGS. 13A-13C, in another implementation, the ultrasonicflow sensor may be a conically shaped probe 132 positioned in thevictim's lower pharynx with the narrow end of the probe seated into thesuperior end of the esophagus in the area of the circular esophagealmuscle 130 and the wide end of the probe just above that in the lowerpharynx. The ultrasonic transducer 131 is located laterally with thebeam directed upward at an angle of approximately 45 degrees from theaxis of the spine. The acoustic beam has been shaped, either by the useof an transducer array or by incorporation of an acoustic lens into theface of the probe, to produce a narrow elevation beam with approximately45 degrees of azimuthal beam angle. The transducer is located in theprobe to cause the acoustic beam to intersect the common carotid arteryand internal jugular vein, and because of the narrow elevation beamangle, will only intersect the carotid and jugular in narrow regions toimprove blood flow velocity accuracy. Blood flow velocity for both thecarotid and jugular are calculated simultaneously with the Dopplershift, 2f_(c)v/c, where f_(c), v and c are the center frequency of theacoustic beam, blood velocity, and the speed of sound, respectively.

With the blood velocity profiles of both the carotid artery and jugularvein calculated, the pulsatility index is calculated as the differenceof the peak aortic velocity and minimum diastolic velocity divided bythe average velocity over one cycle. The Pourcelot, or resistance, indexis calculated as the difference of the peak aortic velocity and minimumdiastolic velocity divided by the peak aortic velocity.

An acoustically reflective material such as aluminum foil 133 laminatedonto a hydrogel may be applied to the patient's neck along the acousticbeam axis to improve the signal detection capability of the transducersystem.

In some implementations, a device tracks the history of CPR times andquality of CPR. This information is used as part of the advisoryalgorithm when the expert system recommends therapy. ECG alone has beenused to classify cardiac rhythms as shockable or non-shockable. However,the success of defibrillation of cardiac pacing may be impacted by thehistory of CPR since ischemic tissue is less likely to depolarize in anorganized way.

Referring to FIGS. 1 and 11, a rescuer uses an AED 10 to automaticallymonitor a victim during cardiac resuscitation. The AED 10 includes aspeaker 6, a display 7, a signal processing module 9 including signalconditioning such as analog filters and an analog to digital converter,a processor 14, and an energy output means 13 such as a defibrillationpulse generator or other pacemaker electrical current or magnetic pulsegenerator. The signal processing module 9 is connected by the ECG signalacquisition module 1 to a set of ECG leads attached to the victim 11.The processor 14 monitors the victim's heart for dangerous rhythms usingthe ECG signals while the victim is resuscitated using chestcompressions techniques. If the AED 10 detects a dangerous heart rhythm,the AED 10 generates an alarm signal. The alarm signal is noticeable tothe rescuer. The AED 10 can generate a defibrillating shock to thevictim when the rescuer issues a command to the AED 10. Thedefibrillating shock is intended to remedy the dangerous rhythm of thevictim's heart.

The AED 10 uses a rhythm advisory method for (a) quantifying thefrequency-domain features of the ECG signals; (b) differentiating normaland abnormal ECG rhythms, such as VF; (c) detecting the onset ofabnormal ECG rhythms; and (d) making decisions about the physiologicalstates of the heart. This frequency-domain measure is reliable with orwithout the presence of the chest compression artifact in the ECGsignals. The AED 10, after identifying the current physiological stateof the heart, can make a decision about appropriate therapeutic actionfor the rescuer to make and communicates the action to the rescuer usingthe speaker 6 and/or the display 7. The display may take the form of agraphical display such as a liquid crystal display (LCD), or may simplybe one or more light emitting diodes or other such visible indicators.Bar-graph indicators such as those contained in LED bar graphs may beparticularly effective at conveying the cyclical, repetitive feedbackdescribed earlier, while at the same time being less expensive, brighterand more easy to read than an LCD display. Separate visible indicators,such as bar graph LEDs, may be utilized for compression and ventilation,so as to minimize confusion on the part of the rescuer.

Referring to FIGS. 12A and 12B, in another implementation, the systemestimation block 55 provided by a physiological model composed of aninterlinked set of difference equations, e.g., as Babbs described above,is used to provide a graphical feedback such as on an LCD display. Theremay be situations during which rescuers are preoccupied withcommunication with other rescuers and may not be able to focus on theircompressions on a compression-by-compression basis necessary to achievethe desired phase synchronization (entrainment). While the lack of phasesynchronization (entrainment) will result in reduced efficacy, there maystill be benefit to be gained by providing to the rescuer a succinctvisual feedback of the four main resuscitation parameters: compressiondepth 114 and rate 115, and ventilation tidal volume 112 and rate 113,on a compression by compression basis. This visual feedback may take theform of separate dials 110, 111, one for compression and one forventilation, provided on a portion of the LCD display of a resuscitationcontrol panel. Each dial may have the two key parameters related to itsperformance displayed on orthogonal axes. Contrasting status bars 115indicate the current status of performance of each of the parameters,while a green central region 116 indicates the desired target zone.Status bars 115 residing either to the right or below the centralregions 116 indicate that the relevant parameter needs to be increasedwhile status bars 115 above or to the right of the central region 116indicate that the relevant parameter needs to be decreased. In somecases, only the ventilation rate may be shown. Alternatively, the dialsmay be composed of additional indicators, e.g., five indicatorscorresponding to: ventilation tidal volume too high and too low;ventilation rate too high and too low; compression depth too deep andtoo shallow; compression rate too fast and too slow; and the two centralregions. If one of the two parameters for a dial is too high or low thatparticular indicator will light while the second parameter that is beingperformed properly will cause the central region 116 to change from redto yellow. When both parameters for a particular dial are beingperformed correctly, the central region will turn green. The indicatorsmay be LEDs or may be regions on an LCD.

These implementations provide a simple physiological model in thefeedback loop. It takes about 35-45 seconds of good chest compressionsto develop good blood flow, yet it only takes 5 seconds for that bloodflow to drop down after the rescuer stops CPR. The problem is thatpeople tend to stop chest compressions too often. By using aphysiological model, e.g., the Babbs model or a more simple one, eachcompression increases an indicator by some amount and that amountdepends on depth of compression. The result is an approximation of theway that actual coronary perfusion pressure reacts for the victim.

As noted, the Babbs physiological models, which have been verified inanimal models and human clinical studies, show that it actually takesapproximately 30-45 seconds of good CPR to bring the coronary perfusionpressure, CPP, up to some decent value. CPP is a measure of the bloodpressure going into the coronary circulation—what supplies blood to theheart muscle. While CPP is slow to rise during compressions, CPP fallsoff precipitously when good CPR stops, within about 10 seconds.

In another implementation, a physiological model is incorporated intothe feedback loop so that what is presented visually to the rescuer is aPerfusion Performance Indicator (PPI), providing them a simple indicatorof the physiological impact of their CPR on the cardiac arrest victim.In a simple implementation, perfusion is modeled as a leaky vessel whichis filled with a certain volume with each compression, that volume beingdependent on the depth of the compression. In between each compression,some of that volume leaks out of the vessel.

The pseudocode listed below implements one possible physiological model.It was desired to bring the Perfusion Performance Indicator to 100% in50 good (proper depth) compressions (about 30 seconds), but at the sametime fall off at a rate that brings the PPI to zero in 15 seconds. Theparticular values chosen were due in part to new CPR guidelines beingproposed by the American Heart Association (AHA) of a 30:2 ratio forcompressions to ventilations. With the pause that typically occurs whenventilations are performed, the PPI will drop significantly by the endof the pause that results from the mid-minute ventilation cycle, but ifgood compressions are performed will be back up to 100 immediately priorto the defibrillation shock that would occur at the end of theone-minute CPR interval. The goal of the rescuer is to get PPI as closeto 100 right before the shock. PPI gets reset to zero after the shock,so the rescuer is motivated to begin compressions immediately aftershock. Another possible graphical feedback implementation is shown inFIG. 12C. The outside thick band (approximately ⅛ inch wide) of PPIblock 120 turns green for 1 second after a good compression (greaterthan 1.5 inch) is delivered, then reverts to black. The band turns redfor 1 second when a “poor” compression (less than 1.5 inch) isdelivered, and then reverts to black again. The goal is to keep the PPIoutline band 120 green. The PPI block 121 “fills up” based onPerfusion_Perf_Ind value (full when Perfusion_Perf_Ind=32896). Morecomplex implementations may incorporate interactions of ventilationswith compressions or more complete models as described by Babbs.

One possible pseudocode implementation is as follows:

Perfusion_Perf_Ind is 0 - 32896 number. CONST DECREMENT_INTERVAL == 100(* number of milliseconds in decrement interval*) DROPOFF == 15 * 1000 /SAMPLE_INTERVAL (* 15 seconds, adjustable*) COMPRESS_RATE == 100(*compressions per minute) NUM_OF_COMPRESS_TO_100_PERCENT == 50;PPI_DECREMENT== 32896 / DROPOFF; IDEAL_INCREASE_PER_COMPRESS == 32896 /50 + ( PPI_DECREMENT * 60 / COMPRESS_RATE) IDEAL_COMPRESS_DEPTH == 2(*inches*) Function { For each decrement interval (for now 100 ms),decrement Perfusion_Perf_Ind by PPI_DECREMENT until Perfusion_Perf_Indequals zero; For each compression detected, if compression depth is > 1inch (*note, NOT 1.5 inches*) { Compression_efficacy = compression depth/ IDEAL_COMPRESS_DEPTH; Perfusion_Perf_Ind = Perfusion_Perf_Ind +IDEAL_INCREASE_PER_COMPRESS * Compression_efficacy; IfPerfusion_Perf_Ind > 32896, then Perfusion_Perf_Ind = 32896; }}

The AED 10 may incorporate functionality for performing additionaltherapeutic actions such as chest compressions, ventilations, ordelivery of intravenous solution containing metabolic or constitutivenutrients. Based on the results of the analysis of the rhythm advisorymethod, the AED 10 may automatically deliver the appropriate therapy tothe patient 11. The AED 10 may also be configured in “advisory” modewherein the AED 10 will prompt the caregiver after the AED 10 has made adetermination of the best therapy, and acknowledgement by thecaregiver/device operator, in the form of a button press orvoice-detected acknowledgement, is required before therapy is deliveredto the patient.

The AED 10 then analyzes the ECG signals to predict defibrillationsuccess as well as to decide whether it is appropriate to defibrillateor to deliver an alternative therapy such as chest compressions, drugssuch as epinephrine, constitutive nutrients such as glucose, or otherelectrical therapy such as pacing.

In some implementations, one or more therapeutic delivery devices 15automatically deliver the appropriate therapy to the patient. Thetherapeutic delivery devices 15 are physically separate from thedefibrillator AED 10 and control of the therapeutic delivery devices 15may be accomplished by a communications link 16. The communications link16 may take the form of a cable connecting the devices but preferablythe link 16 is via a wireless protocol such as Bluetooth or a wirelessnetwork protocol such as Institute of Electrical and ElectronicsEngineers (IEEE) 802.11. The therapeutic delivery device 16 can be aportable chest compression device that is commercially available as theAutopulse™, provided by Revivant of Sunnyvale, Calif. In other examples,the therapeutic delivery device 16 is a drug infusion device that iscommercially available as the Power Infuser™, provided by InfusionDynamics of Plymouth Meeting, Pa., or the Colleague CX™, provided byBaxter Healthcare Corp., of Round Lake, Ill. The therapeutic deliverydevice 16 can be a ventilator that is commercially available as theiVent™, provided by Versamed of Pearl River, New York. The therapeuticdelivery device 16 can also include multiple therapies such asdefibrillation, chest compression, ventilation and drug infusion.

In other implementations, control and coordination for the overallresuscitation event and the delivery of the various therapies may beaccomplished by a device 17 or processing element external to the AED10. For instance, the device 17 may download and process the ECG datafrom the AED 10, analyze the ECG signals, perform the determinationsbased on the analysis, and control the other therapeutic devices 16,including the AED 10.

In other implementations, the AED 10 may perform all the processing ofthe ECG, including analyzing the ECG signals, and transmit to thecontrol device 17 only the final determination of the appropriatetherapy, whereupon the control device 17 would perform the controlactions on the other linked devices 30. The control device 17 preferablyis a laptop computer running automated patient record software such asTablet PCR, manufactured by ZOLL Data Systems of Denver, Colo.

Many other implementations of the invention other than those describedabove are within the invention, which is defined by the followingclaims.

What is claimed is:
 1. A method of measuring blood flow in the vertebralartery during cardiac resuscitation, the method comprising positioningan ultrasonic probe at the end of a catheter, inserting the probe andcatheter into or at the opening of the esophagus, wherein the probe isconfigured to deliver and measure ultrasonic sound energy posteriorlytoward the spine at approximately the cervical vertebra C3-C6, toestimate blood flow in the vertebral artery; and using a processor,memory, and associated circuitry to process the output of the probe toestimate blood flow achieved by CPR chest compressions in the vertebralartery by ultrasonic Doppler flow measurement.
 2. The method of claim 1wherein the sensor is combined with a cardiac resuscitation device andthe estimated blood flow determined from the sensor output is used indetermining the timing of feedback cues delivered to the user.